How to Use the Winston AI MCP in Mastra AI

Q: If I use Winston AI for fact-checking with Mastra AI, what's the process?

You set up a flow: check facts on a URL using factcheckerurl. If that fails or returns low confidence, your workflow can automatically retry by running factcheckerfile next.

Q: Does Winston AI support detecting AI content with Mastra AI?

Yes. You'll use the detection tools like detectaitext. This function is great for building audit workflows that require multiple attempts or specific failure handling logic.

Build reliable, multi-step workflows with Mastra AI's advanced workflow engine.

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Setup Winston AI with Mastra AI

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Plagiarism Checking Workflows

You can set up a sequence of checks using `check_plagiarism_text` and then conditionally run an action based on the score. Mastra AI's workflow engine lets you define failure paths, like retrying with different credentials. Need to check multiple sources? Combine `check_plagiarism_url` with other steps in your workflow. If one URL fails, the system doesn't crash; it executes a fallback plan.

AI Detection and Fact Verification

Use `detect_ai_file` as part of an approval flow. For instance, if a document is flagged by AI detection, you can trigger a notification step to an admin user. Mastra AI handles this conditional branching automatically. You can sequence fact-checking: run `fact_checker_url`, and if the result is ambiguous, automatically move to running `text_compare` against a known source.

Advanced Text Comparison

The `text_compare` tool lets you check for similarity between two pieces of content. Within Mastra AI, this comparison can be tied into a payment workflow; if the similarity is too high (potential copyright issue), it triggers an immediate rejection and escalation. This capability helps build robust processes that manage data types like text strings by enforcing specific business rules through automatic retries.

Setup guide

Set up Winston AI MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Winston AI tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "winston-ai-mcp-client",
  servers: {
    "winston-ai-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Winston AI Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Winston AI tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Winston AI transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Winston AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Winston AI MCP in Mastra AI

You pass the MCP server endpoint to Mastra AI. The framework then treats all tools, like `fact_checker`, as nodes in a complex graph. This lets you build automated processes that handle failure gracefully.

Absolutely. You can create a workflow where the first step runs `check_plagiarism_text`. Based on the score, you'll branch: if it passes, proceed to approval; if not, notify the user immediately.

The server deals with multiple types of Content. This includes text strings, document URLs, and image detection via URL. The workflow engine ensures that the handling of this sensitive media data follows strict, defined paths.

You set up a flow: check facts on a URL using `fact_checker_url`. If that fails or returns low confidence, your workflow can automatically retry by running `fact_checker_file` next.

Yes. You'll use the detection tools like `detect_ai_text`. This function is great for building audit workflows that require multiple attempts or specific failure handling logic.

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